Only one fusion matters: Spatiotemporal Weighted Integration for one-shot Federated Traffic Prediction

As the cognitive engine of urban Cyber-Physical Systems (CPS), accurate traffic prediction is essential for optimizing physical flows through cybernetic control. Cross-city traffic prediction is critical for intelligent transportation systems but faces a fundamental dilemma: purely local training suffers from data scarcity in emerging cities, while centralized training compromises data privacy. FL offers a decentralized learning alternative, yet traditionally struggles with significant communication overhead and domain heterogeneity. Existing methods typically rely on iterative, multi-round parameter synchronization to align disparate traffic patterns, which is often infeasible in bandwidth-constrained cyber–physical systems. In this paper, we propose SWIFT ( S patiotemporal W eighed I ntegration for F ederated T raffic Prediction), a communication-efficient framework that achieves high-performance traffic prediction with only One-shot of communication. Unlike methods that blindly aggregate these conflicting features, SWIFT explicitly decomposes them into orthogonal semantic subspaces. This disentanglement ensures that only semantically aligned temporal knowledge is shared, effectively eliminating the need for iterative correction. Furthermore, the framework incorporates a context-aware dynamic gating mechanism that adaptively weights and fuses these decoupled features according to real-time traffic conditions. Extensive experiments on four real-world datasets demonstrate that SWIFT matches the performance of mature multi-round FL methods, offering a scalable paradigm for data-scarce urban environments.